A BIBLIOMETRIC EXPLORATION OF NEGATIVE REVIEWS AS CATALYSTS FOR QUALITY IMPROVEMENT

Land Forces Academy Nicolae Balcescu of Sibiu and Lucian Blaga University of Sibiu, Romania
Romania


Abstract

In an economic landscape increasingly defined by uncertainty and complexity, traditional quality management strategies often struggle to keep pace with rapidly shifting consumer expectations. This paper investigates an information asset that remains frequently underutilized during periods of crisis: negative customer feedback. Far from being a mere indicator of failure, negative reviews function as real-time market sensors, providing a critical map of product and process vulnerabilities. The research employs a hybrid methodology centered on a rigorous bibliometric analysis conducted using the bibliometrix package in the R software. By processing metadata from articles indexed in Web of Science the study maps the conceptual evolution of consumer dissatisfaction, tracing its transition from a perceived marketing barrier to a strategic catalyst for quality-driven innovation. The findings reveal a strong correlation between "negative sentiment" and "innovation agility," demonstrating that organizations capable of thriving in volatile economies are those that integrate complaints directly into the Plan-Do-Check-Act (PDCA) quality improvement cycle. The paper proposes a strategic framework in which the "critical voice" of the customer serves as fuel for resource optimization and the mitigation of nonconformity. The conclusions emphasize that, under conditions of uncertainty, competitiveness is not derived from the absolute avoidance of errors, but from the organizational capacity to transform digitally signaled failures into targeted, incremental improvement. This approach redefines quality management not as a state of static compliance, but as a dynamic and resilient dialogue with the market.

Keywords



Full Text


References


Barravecchia, F., Mastrogiacomo, L., & Franceschini, F. (2025a). Detecting digital voice of customer anomalies to improve product quality tracking. International Journal of Quality and Reliability Management. https://doi.org/10.1108/IJQRM-07-2024-0229

Barravecchia, F., Mastrogiacomo, L., & Franceschini, F. (2025b). Digital VoC analysis for product/service quality tracking in the era of Quality 4.0. International Journal of Quality and Reliability Management, 1–17. https://doi.org/10.1108/IJQRM-01-2025-0027

Chan, S., Amin, M., Rasool, S., & Syed, O. R. (2025). From click to confirmation. The effect of hotel website quality and online reviews in fostering booking intentions. International Journal of Quality and Service Sciences, 17(2), 220–242. https://doi.org/10.1108/IJQSS-11-2024-0171

Chliova, M., Cacciotti, G., Kautonen, T., & Pavez, I. (2025). Reacting to criticism: What motivates top leaders to respond substantively to negative social performance feedback? Journal of Business Research, 186. https://doi.org/10.1016/j.jbusres.2024.115005

Dakša, G., & Kokina, K. (2025). Supplementing Tap Water Quality Monitoring Through Customer Feedback: A GIS-Centered Approach. Water (Switzerland), 17(21). https://doi.org/10.3390/w17213103

Devi, N. L., Anilkumar, B., Sowjanya, A. M., & Kotagiri, S. (2024). An innovative word embedded and optimization based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews. Multimedia Tools and Applications, 83(33), 79303–79336. https://doi.org/10.1007/s11042-024-18510-7

Ershadi, M. J., Najafi, N., & Soleimani, P. (2019). Measuring the impact of soft and hard total quality management factors on customer behavior based on the role of innovation and continuous improvement. The TQM Journal, 31(6), 1093–1115. https://doi.org/10.1108/TQM-11-2018-0182

Hakimi, M., Haq, M. A. U., Ghouri, A. M., & Valette-Florence, P. (2025, December 1). Analyzing customer reviews with abstractive summarization and sentiment analysis: a software review. Journal of Marketing Analytics, Vol. 13, pp. 1271–1285. Palgrave Macmillan. https://doi.org/10.1057/s41270-025-00377-8

Hossain, Md Shamim, & Rahman, Mst Farjana. (2023). Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches. FIIB Business

Review, 12(4), 386–402. https://doi.org/10.1177/23197145221115793

Hsiao, Y. H., Chen, M. C., Hong, M. Z., & Huang, Y. T. (2025). Service improvement through value co-creation framework for e-tailing logistics with analytics on customer complaints. Research in Transportation Business & Management, 61, 101416. https://doi.org/10.1016/J.RTBM.2025.101416

Hsueh, J.-T., & Hsu, S.-H. (2024). Turning negative reviews into operational insights: ABSS-GPT’s role in informing hotel decisions. Journal of Decision Systems, 1–16. https://doi.org/10.1080/12460125.2024.2428977

Joung, J., & Kim, H. M. (2021). Approach for Importance–Performance Analysis of Product Attributes From Online Reviews. Journal of Mechanical Design, 143(8). https://doi.org/10.1115/1.4049865

Kim, S. A., Park, S., Kwak, M., & Kang, C. (2025a). Examining product quality and competitiveness via online reviews: An integrated approach of importance performance competitor analysis and Kano model. Journal of Retailing and Consumer Services, 82, 104135. https://doi.org/10.1016/J.JRETCONSER.2024.104135

Kim, S. A., Park, S., Kwak, M., & Kang, C. (2025b). Examining product quality and competitiveness via online reviews: An integrated approach of importance performance competitor analysis and Kano model. Journal of Retailing and Consumer Services, 82, 104135. https://doi.org/10.1016/J.JRETCONSER.2024.104135

Krishna, E. S. P., Ramu, T. B., Chaitanya, R. K., Ram, M. S., Balayesu, N., Gandikota, H. P., & Jagadesh, B. N. (2025). Enhancing E-commerce recommendations with sentiment analysis using MLA-EDTCNet and collaborative filtering. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91275-7

Liao, Y. Y., Soltani, E., Iqbal, A., & van der Meer, R. (2024). The utility of performance review systems: A total quality management perspective. Strategic Change, 33(4), 287–310. https://doi.org/https://doi.org/10.1002/jsc.2580

Liu, W., Wang, Z., & Zhao, H. (2020). Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview. Electronic Markets, 30(4), 735–757. https://doi.org/10.1007/s12525-020-00395-7

Mikul, & Mittal, I. (2024). Mapping the research field on product quality: a bibliometric analysis. International Journal of Quality & Reliability Management, 41(7), 1729–1751. https://doi.org/10.1108/IJQRM-08-2023-0259

Nilashi, M., Abumalloh, R. A., Ahmadi, H., Samad, S., Alrizq, M., Abosaq, H., & Alghamdi, A. (2023). The nexus between quality of customer relationship management systems and customers’ satisfaction: Evidence from online customers’ reviews. Heliyon, 9(11), e21828. https://doi.org/10.1016/J.HELIYON.2023.E21828

Skačkauskienė, I., & Nekrosiene, J. (2022). MARKETING EFFECTIVENESS EVALUATION POSSIBILITIES AND CHALLENGES FOR BUSINESS: A BIBLIOMETRIC ANALYSIS. 12th International Scientific Conference “Business and Management 2022.” Vilnius Gediminas Technical University. https://doi.org/10.3846/bm.2022.836

Soane, E. (2024). Connecting risk, management and organisational goals: integrating risk with organisations’ systems to enhance performance and competitive advantage. Journal of Risk Research, 27(12), 1589–1604. https://doi.org/10.1080/13669877.2025.2485041

Srivastava, M., & Sivaramakrishnan, S. (2021). Mapping the themes and intellectual structure of customer engagement: a bibliometric analysis. Marketing Intelligence & Planning, 39(5), 702–727. https://doi.org/10.1108/MIP-11-2020-0483

Treadgold, B. M., Coulson, N. S., Campbell, J. L., Lambert, J., & Pitchforth, E. (2025). Quality and Misinformation About Health Conditions in Online Peer Support Groups: Scoping Review. J Med Internet Res, 27, e71140. https://doi.org/10.2196/71140

Wang, Y., Guan, Z., & Feng, L. (2025). Optimal Pricing and Launching Strategy for Quality-Differentiated Products in the Presence of Online Reviews. IEEE Transactions on Engineering Management, 72, 1909–1923. https://doi.org/10.1109/TEM.2025.3562634

Wang, Z., Ji, Y., Zhang, T., Li, Y., Wang, L., & Qu, S. (2022). Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research. Data Technologies and Applications, 57(4), 437–464. https://doi.org/10.1108/DTA-03-2022-0124

Yazıcı, G., & Ozansoy Çadırcı, T. (2024a). Creating meaningful insights from customer reviews: a methodological comparison of topic modeling algorithms and their use in marketing research. Journal of Marketing Analytics, 12(4), 865–887. https://doi.org/10.1057/s41270-023-00256-0

Yazıcı, G., & Ozansoy Çadırcı, T. (2024b). Creating meaningful insights from customer reviews: a methodological comparison of topic modeling algorithms and their use in marketing research. Journal of Marketing Analytics, 12(4), 865–887. https://doi.org/10.1057/s41270-023-00256-0

Zhang, D., & Wang, Z. (2025). Consumer complaints as catalysts: driving firms’ total factor productivity enhancement. Finance Research Letters, 86, 108864. https://doi.org/10.1016/J.FRL.2025.108864

Zheng, L., Sun, L., He, Z., & He, S. (2025). Dynamic product quality improvement using social media data and competitor-based Kano model. International Journal of Production Economics, 285, 109645. https://doi.org/10.1016/J.IJPE.2025.109645

Zhu, Y., & Liang, G. (2024). Designing product upgrades in the presence of online consumer reviews. Managerial and Decision Economics, 45(6), 3915–3928. https://doi.org/https://doi.org/10.1002/mde.4232

Ziegler, A., Peisl, T., & Raeside, R. (2023). Improving service quality through customer feedback – the case of NPS in IBM’s training services. International Journal of Quality and Service Sciences, 15(2), 190–203. https://doi.org/10.1108/IJQSS-09-2022-0106